where \(D\)  is the graph degree matrix defined on the 2D image pixel grid and \(K\)  is the number of samples used to approximate the expectation, with \(K=1\)  in our experiments. We let \(L=D-A\)  be the Laplacian of a neighborhood graph defined on the pixel grid, where \(A\)  is a pixel neighborhood adjacency matrix. To encourage the spatial smoothness of SVF \(z\) , we set \(\Lambda _{z}=\Sigma _{z}^{-1}= \lambda L\)  [1]}, where \(\lambda \)  is a parameter controlling the scale of the SVF \(z\) .
